Class Syllabus
(old one)
Description:
An introduction to probability theory and statistics, with an emphasis on solving problems in computer science and engineering. Probability and statistics is an important foundation for computer science fields such as machine learning, artificial intelligence, computer graphics, randomized algorithms, image processing, and scientific simulations. Topics in probability include discrete and continuous random variables, probability distributions, sums and functions of random variables, the law of large numbers, and the central limit theorem. Topics in statistics include sample mean and variance, estimating distributions, correlation, regression, and hypothesis testing. Beyond the fundamentals, this course will also focus on modern computational methods such as simulation and the bootstrap. Students will learn statistical computing using the freely available R statistics software.

Book:A Modern Introduction to Probability and Statistics: Understanding Why and How by Dekking, Kraaikamp, Lopuhaa, and Meester.
An electronic version of this book is freely available through the University: here.
To access the book you must be visiting this website from the campus network. Or if you are off campus, you can access it using VPN.

Homeworks can be turned in as a hard copy at the start of the class in which they are due. Or they can be turned in through Canvas as a pdf by the start of class. If you choose to create a pdf, I strongly suggest using Latex. Some helpful materials can be found here: latex.

Schedule:(subject to change - linked material from old version of class marked as (2013), may be slightly updated)